Xue Haosen, Lu Zeying, Lan Yueheng, Gui Lili, Sun Xiaojuan
School of Science, Beijing University of Posts and Telecommunications, Beijing, China.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
PLoS One. 2024 Dec 20;19(12):e0314962. doi: 10.1371/journal.pone.0314962. eCollection 2024.
Neuromodulation plays a critical role in the normal physiological functions of organisms. With advancements in science and technology, neuromodulation has expanded into various fields. For instance, in the field of engineering, in vitro-cultured neural networks are utilized to perform closed-loop control for achieving complex functionalities. Conducting pioneering theoretical research using mathematical models is particularly essential for enhancing efficiency and reducing costs. This study focuses on examining the relationship between input and output in order to establish a groundwork for more advanced closed-loop regulation applications in engineering. Using a constructed neural network model, Poisson, square wave and direct current (DC) stimulation are applied. The results show that the network's firing rate increases with the frequency or amplitude of these stimulations. And the network's firing rate could reach to a stable state after the stimulation is applied for 0.8s and return to initial states when the stimulus is removed for 1s. To ascertain if the system exhibits a memory effect from the previous stimulus, we conduct independent and continuous stimulation schemes. Comparing the firing rate of neuronal networks under these two stimulation schemes reveals a memory effect of the system on the previous stimulus, which is independent of network properties and stimulus types. Finally, by applying square wave stimulation to the in vitro cultured neural network, we have confirmed that cultured neural network actually can reach to a steady state and have memory effects on the previous stimulus. Our research results have important theoretical significance and reference value for designing the closed-loop regulation strategy of in vitro cultured neuronal networks.
神经调节在生物体的正常生理功能中起着关键作用。随着科学技术的进步，神经调节已扩展到各个领域。例如，在工程领域，利用体外培养的神经网络进行闭环控制以实现复杂功能。利用数学模型开展开创性理论研究对于提高效率和降低成本尤为重要。本研究着重考察输入与输出之间的关系，以便为工程中更先进的闭环调节应用奠定基础。使用构建的神经网络模型，施加泊松、方波和直流（DC）刺激。结果表明，网络的放电频率随这些刺激的频率或幅度增加。并且在施加刺激0.8秒后网络的放电频率可达到稳定状态，当去除刺激1秒后恢复到初始状态。为确定系统是否对先前刺激表现出记忆效应，我们进行独立且连续的刺激方案。比较这两种刺激方案下神经元网络的放电频率揭示了系统对先前刺激的记忆效应，这与网络特性和刺激类型无关。最后，通过对体外培养的神经网络施加方波刺激，我们证实培养的神经网络实际上可以达到稳定状态并对先前刺激具有记忆效应。我们的研究结果对于设计体外培养神经元网络的闭环调节策略具有重要的理论意义和参考价值。
